Infrared thermographic SAR measurements of interstitial hyperthermia applicators: errors due to thermal conduction and convection
Bibliographic record
Abstract
Thermal conduction and convection were examined as sources of error in thermographically measured SAR patterns of an interstitial microwave hyperthermia applicator. Measurements were performed in a layered block of muscle-equivalent phantom material using an infrared thermographic technique with varying heating duration. There was a 52.7% reduction in maximum SAR and 75.5% increase in 50% iso-SAR contour area for a 60-s heating duration relative to a 10-s heating duration. A finite element model of heat transfer in an homogeneous medium was used to model conductive and convective heat transfer during the thermographic measurement. Thermal conduction artefacts were found to significantly distort thermographically measured SAR patterns. Convective cooling, which occurs when phantom layers are exposed for thermal image acquisition, was found to significantly affect the magnitude, but not the spatial distribution, of thermographically measured SAR patterns. Results from this investigation suggest that the thermal diffusion artefacts can be minimized if the duration of the applied power pulse is restricted to 10 s or less.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".